Job Description

[What the role is]

The Singapore Botanic Gardens (SBG) is one of the oldest tropical botanical gardens and one of only three botanic gardens in the world inscribed as a UNESCO World Heritage Site. The SBG is a Division of the National Parks Board, the statutory agency responsible for managing greenery and biodiversity in Singapore, SBG houses the Botanical Research Branch (BR), where this position will be based.

The Singapore Herbarium at SBG holds some of the oldest and most important records from Singapore, Malaysia, and Southeast Asia, and is undergoing a digital transformation and is currently digitalizing its entire Herbarium collection. The images and data from these specimens can be used to understand the coverage (through time and space) of the Herbarium collections and exploited in new ways to develop and examine a range of research questions. SBG also conducts long-term phenological monitoring of trees in the living collections of SBG as well as a two-year monitoring of litterfall and reproductive phenology in permanent plots in Singapore’s nature reserves and parks.

[What you will be working on]

Responsibilities

You will:

• Develop and pilot workflows that leverage data from Herbarium images and transcribed label data, making them accessible to researchers across SBG so that non-specialists can generate their own models and explore research questions independently.

• Use Herbarium data to pilot approaches across three areas: i) assessing data quality, including curation standards and taxonomic naming consistency, and testing species identification from global collection datasets using machine learning; ii) exploring collection gaps in SE Asia through mapping and spatial modelling; and iii) applying AI tools to query images for signs of taxonomic heterogeneity and surface potential new research questions across selected target taxa.

• Query the Herbarium data as well as field phenological observations to pilot an investigation into changes in the timing and duration of phenological episodes from the mid-to-late nineteenth century to the present, with the aim of better understanding climate change effects on future biodiversity outcomes in Singapore and the region.

• Build pilot workflows for: i) generating morphological descriptions and identification keys from images to support rapid species description for Flora accounts and journal submissions, including species new to science; and ii) querying published taxonomic accounts to test the integrity of specimen determinations in the Herbarium image dataset.

• Collaborate internally with colleagues to generate outputs for use in other models and to translate research into action;

• Publish in reputable peer-reviewed journals and present at scientific conferences.

[What we are looking for]

• Possess a PhD in Botany or a related discipline, preferably. Candidates without a PhD should have a strong track record in artificial intelligence, data science, computer vision and/or natural history collection digitisation.

• Have experience applying artificial intelligence and data science, particularly computer vision, to herbarium or other natural history collection digitisation projects.

• Demonstrate ability to translate research into working tools; software engineering know-how will be an advantage.

• Have a keen interest in the plants of tropical Asia, with a demonstrated record of publishing in reputable scientific journals being advantageous.

• Possess excellent written and verbal communication skills.

• Be self-motivated, organised, independent, and able to work well with others under time pressure.

Only shortlisted candidates will be notified. The successful candidate will be offered a contract until 31 December 2028.